The paper demonstrates that the routing state accumulated by adaptive hierarchical systems inherently provides a coherent attribution across the hierarchy. A leaf node’s contribution is the product of local routing weights along its path, while an internal node receives the corresponding prefix product, ensuring that finer readouts sum exactly to coarser ones. This attribution reflects the router’s learned preferences rather than intrinsic component values, and is shown to reveal meaningful structure at multiple levels in LLM, Census, agentic, and telecom-network hierarchies, with higher‑level readouts often offering clearer organization than leaf‑level ones.
arXiv:2606.20621v2 Announce Type: replace Abstract: Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often intro...
arXiv:2608. 14641v1 Announce Type: new Abstract: Agentic systems increasingly delegate model selection to a router, yet open-source routers are usually evaluated with different tasks, candidate pools, and execution protocols, limiting direct comparison.
arXiv:2609.37362v1 Announce Type: new Abstract: Large language model (LLM) routing aims to assign each query to the most suitable model from a heterogeneous candidate pool, improving the quality--eff...
arXiv:2608. 11340v1 Announce Type: cross Abstract: Symbolic network verifiers can reason about correctness across vast spaces of routing inputs and failures, but only for the protocols and features an expert has encoded by hand.
arXiv:2610.09981v1 Announce Type: cross Abstract: LLM routers pick a cheap or expensive model per request by its content, and many gateways and some cloud platforms can log that choice with content l...
The paper introduces a two‑level readout for mixture‑of‑experts reasoning models. First, it compresses the model’s internal reasoning states into a 64‑dimensional semantic frame (J64) that reveals process dynamics beyond the emitted trace. Second, it reconstructs this frame from native expert‑routing statistics (R64), achieving high correlation and preserving most predictive gains while enabling low‑overhead, test‑time decision making.
The paper introduces Routed Graph Handoff, a lightweight LLM router that chooses between a typed dependency graph and natural language for each delegation in multi‑agent LLM systems. On four benchmarks with over 1,050 trajectories, the routed system matches or surpasses natural‑language‑only performance, achieving significant compression and accuracy gains. The approach requires a graph‑aware executor prompt, and an oracle analysis suggests further potential for execution‑time adaptive routing.
arXiv:2608. 11218v1 Announce Type: new Abstract: Independent agents that reason in latent space can share computed state as key-value cache fragments rather than text.
The paper investigates whether a frozen transformer can use another model’s KV cache as a substitute for its own, finding that this approach fails because the reader only accepts caches within a narrow tolerable set. Experimental results show that none of eight substitution methods improve performance over a baseline, while a different strategy—adding the sender’s cache as extra context and only adapting queries—does outperform the baseline. The authors conclude that pursuing faithful cache substitution is misguided and that augmenting the reader’s own cache is the more promising direction.
arXiv:2609.14773v1 Announce Type: new Abstract: As LLM conversations grow to hundreds of turns, full-context injection incurs $O(N^2)$ cumulative token costs, while lossy summarization or hard trunca...
The paper introduces Adaptive Influence Graphs (AIGs), a two‑stage framework that first converts a failed trace into a structured graph and then navigates it to pinpoint the critical error in multi‑agent large language model systems. Experiments across multiple models demonstrate that richer trace representations and adaptive graph construction improve failure attribution, with AIGs achieving state‑of‑the‑art results on the Who&When benchmark. The study shows that both the diagnosing model and the way traces are represented and explored are crucial for accurate failure attribution.
ROUTEAUDIT presents a framework for identifying and attributing performance in adaptive multi‑verifier routing systems. It treats routing as a contract‑conditioned identification problem, adding measurable objects such as a contract lattice, a policy‑independent response tape, and request‑level bounds to capture path sensitivity and finite‑population intervals. Experiments on held‑out caches and task requests demonstrate improved quality metrics and precise attribution with low reconstruction error.